import sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()

results_folder = 'mmvec_major_taxa_scrambled'
results_base_name = 'latent_dim_3_input_prior_1.00_output_prior_1.00_beta1_0.90_beta2_0.95'

table = pd.read_table(results_folder + '/' + results_base_name + '_ranks.txt', index_col=0)
table.head()

	Propionibacteriaceae	Staphylococcus caprae or capitis	Staphylococcus epidermidis	Staphylococcus hominis	Other Staphylococci	Polyomavirus HPyV6	Polyomavirus HPyV7	Merkel Cell Polyomavirus	Malasseziaceae	Corynebacteriaceae	Micrococcaceae	Other families
featureid
X940001	0.108002	0.281476	0.230751	-0.088645	0.325523	0.159858	0.003797	0.040958	0.541223	0.191130	0.089953	0.273742
X940002	-0.311842	-0.030911	0.145908	-0.223350	0.145753	0.025062	-0.045640	0.148346	-0.102332	0.175697	-0.079407	0.127062
X940005	-0.144484	-0.046149	-0.087393	-0.239615	-0.052742	-0.087412	-0.162386	-0.153883	0.000429	-0.073136	-0.126851	0.190019
X940007	0.219706	0.499016	0.520032	0.120489	0.626079	0.455443	0.365660	0.403363	0.544929	0.532255	0.324215	0.925777
X940010	0.497619	0.113296	0.059617	0.605515	-0.038072	0.152955	0.305821	0.190868	0.073865	0.026769	0.315798	-0.430855

table['Selected'] = np.isin(table.index,
                            ['X940203', 'X940589', 'X940625', 'X940925', 'X940936', 'X942191',
                             'X942237', 'X950023', 'X950028', 'X950056', 'X950157', 'X950173',
                             'X950193', 'X950225', 'X950228', 'X950233', 'X950254', 'X950396',
                             'X950485', 'X950584', 'X950661', 'X950999', 'X960035', 'X960242',
                             'X960306', 'X960421', 'X960463', 'X960465', 'X960712', 'X960726',
                             'X960934', 'X961553', 'X961686', 'X970018', 'X970091', 'X970092',
                             'X970232', 'X970283', 'X970327', 'X970342', 'X970633', 'X970680']
                           )
table.sort_values('Selected', inplace=True)
sns.relplot(
    table,
    y='Propionibacteriaceae', x='Staphylococcus epidermidis', hue='Selected'
)

<seaborn.axisgrid.FacetGrid at 0x7fa01ea0b410>

sns.pairplot(table, hue='Selected')

<seaborn.axisgrid.PairGrid at 0x7fa01ea2c810>

for i in table.columns[:-1]:
    sns.displot(table, x=i, hue='Selected', multiple='stack')